Keep ai model saved when app closes.

Ran into an issue with the previous model for having mllama archetecture so I changed it

TODO.md

@@ -1 +1 @@
-In the future, planned updates include a re-vamp of the settings panel, text box's (important, tip, warning, and note) are not properly moving to the intended areas and I will look into locking the size (or being able to control the size) of images throughout varias export formats.
+In the future, planned updates include a re-vamp of the settings panel, text box's (important, tip, warning, and note) are not properly moving to the intended areas and I will look into locking the size (or being able to control the size) of images throughout varias export formats. OCR or local ai would be pretty cool....

app/renderer/dialogs.js

@@ -318,8 +318,13 @@ function showSettingsDialog({
     const aiAutoDoc = el('input', { type: 'checkbox', checked: Boolean(settings.ai?.autoDoc) });
     const ollamaHost = makeInput(settings.ai?.ollama?.host || 'http://127.0.0.1:11434');
     const ollamaModel = makeInput(settings.ai?.ollama?.model || 'llama3.2:1b');
-    const aiStatus = el('div', { className: 'muted ai-status' }, 'AI stays local through Ollama. Nothing is sent to the cloud.');
+    const aiStatus = el('div', { className: 'muted ai-status' }, 'AI stays local through Ollama. Vision-capable models can also inspect the screenshot attached to each step.');
     const testAiBtn = el('button', { type: 'button' }, 'Test connection');
+    const persistOllamaModel = debounce(() => {
+      const model = ollamaModel.value.trim();
+      // Keep the last model choice even if the dialog is dismissed without a full save.
+      void api.settings.set({ keyPath: 'ai.ollama.model', value: model }).catch(() => {});
+    }, 250);
 
     const updateAiStatus = (message, { error = false } = {}) => {
       aiStatus.textContent = message;
@@ -341,7 +346,9 @@ function showSettingsDialog({
           return;
         }
         if (result.installed) {
-          updateAiStatus(`Connected to ${result.host} with ${result.model}.`);
+          updateAiStatus(result.vision
+            ? `Connected to ${result.host} with ${result.model}. It can inspect screenshots.`
+            : `Connected to ${result.host} with ${result.model}. This model is text-only, so StepForge will use OCR and metadata only.`);
         } else {
           updateAiStatus(`Connected to ${result.host}. Model ${result.model} is not installed yet.`, { error: true });
         }
@@ -351,6 +358,8 @@ function showSettingsDialog({
         setButtonLoading(testAiBtn, false);
       }
     };
+    ollamaModel.addEventListener('input', () => persistOllamaModel());
+    ollamaModel.addEventListener('blur', () => persistOllamaModel.flush());
 
     const placeholderRows = el('div', { className: 'placeholder-rows' });
     const rows = [];

app/text-intel.js

@@ -35,6 +35,15 @@ function clamp(v, min, max) {
   return Math.min(max, Math.max(min, v));
 }
 
+function modelLooksVisionCapable(model) {
+  const clean = normalizeWhitespace(model).toLowerCase();
+  if (!clean) return false;
+  return clean.includes('vision')
+    || clean.includes('llava')
+    || clean.includes('gemma4')
+    || /(^|[^a-z0-9])qwen[23](?:\.[0-9]+)?vl([^a-z0-9]|$)/.test(clean);
+}
+
 let createWorkerImpl = null;
 function loadCreateWorker() {
   if (createWorkerImpl) return createWorkerImpl;
@@ -64,6 +73,7 @@ class TextIntelService {
     this.workerPromise = null;
     this.workerQueue = Promise.resolve();
     this.ocrDataDir = path.join(dataDir, 'ocr', 'eng');
+    this.modelCapabilityCache = new Map();
   }
 
   async shutdown() {
@@ -372,15 +382,63 @@ public static class Win32 {
     const data = await res.json();
     const models = Array.isArray(data?.models) ? data.models.map((model) => model.name).filter(Boolean) : [];
     const installed = config.ollama.model ? models.includes(config.ollama.model) : false;
+    const vision = installed ? await this.modelSupportsVision({
+      host: config.ollama.host,
+      model: config.ollama.model,
+    }) : false;
     return {
       ok: true,
       installed,
+      vision,
       models,
       host: config.ollama.host,
       model: config.ollama.model,
     };
   }
 
+  async modelCapabilities({ host, model }) {
+    const normalizedHost = normalizeOllamaHost(host);
+    const normalizedModel = normalizeWhitespace(model);
+    if (!normalizedHost || !normalizedModel) return [];
+    const cacheKey = `${normalizedHost}::${normalizedModel}`;
+    if (this.modelCapabilityCache.has(cacheKey)) {
+      return this.modelCapabilityCache.get(cacheKey);
+    }
+    const url = new URL('/api/show', `${normalizedHost.replace(/\/+$/, '')}/`);
+    let capabilities = [];
+    try {
+      const response = await this.fetch(url, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ model: normalizedModel }),
+      });
+      if (response.ok) {
+        const payload = await response.json();
+        capabilities = Array.isArray(payload?.capabilities)
+          ? payload.capabilities.map((cap) => normalizeWhitespace(cap).toLowerCase()).filter(Boolean)
+          : [];
+      }
+    } catch {
+      capabilities = [];
+    }
+    if (!capabilities.includes('vision') && modelLooksVisionCapable(normalizedModel)) {
+      capabilities = [...capabilities, 'vision'];
+    }
+    this.modelCapabilityCache.set(cacheKey, capabilities);
+    return capabilities;
+  }
+
+  async modelSupportsVision({ host, model }) {
+    const capabilities = await this.modelCapabilities({ host, model });
+    return capabilities.includes('vision');
+  }
+
+  readStepImageBase64(guideId, stepId) {
+    const imagePath = this.store.stepImagePath(guideId, stepId, 'working') || this.store.stepImagePath(guideId, stepId, 'original');
+    if (!imagePath || !fs.existsSync(imagePath)) return '';
+    return fs.readFileSync(imagePath).toString('base64');
+  }
+
   async callOllamaText({ host, model, prompt, systemPrompt }) {
     const url = new URL('/api/chat', `${host.replace(/\/+$/, '')}/`);
     const response = await this.fetch(url, {
@@ -403,8 +461,12 @@ public static class Win32 {
     return content.trim();
   }
 
-  async callOllama({ host, model, prompt, systemPrompt }) {
+  async callOllama({ host, model, prompt, systemPrompt, images = [] }) {
     const url = new URL('/api/chat', `${host.replace(/\/+$/, '')}/`);
+    const userMessage = { role: 'user', content: prompt };
+    if (Array.isArray(images) && images.length) {
+      userMessage.images = images;
+    }
     const response = await this.fetch(url, {
       method: 'POST',
       headers: { 'Content-Type': 'application/json' },
@@ -414,7 +476,7 @@ public static class Win32 {
         format: 'json',
         messages: [
           { role: 'system', content: systemPrompt },
-          { role: 'user', content: prompt },
+          userMessage,
         ],
         options: {
           temperature: 0.2,
@@ -460,6 +522,14 @@ public static class Win32 {
         return { ok: false, reason: 'Block not found.' };
       }
 
+      const screenshotBase64 = step.image ? this.readStepImageBase64(guideId, stepId) : '';
+      const screenshotAttached = Boolean(screenshotBase64)
+        ? await this.modelSupportsVision({
+          host: config.ollama.host,
+          model: config.ollama.model,
+        })
+        : false;
+
       let captureContext = null;
       // Use stored capture metadata when available (best context, from capture time).
       // Fall back to re-running OCR on the stored image only when metadata is absent.
@@ -514,6 +584,7 @@ public static class Win32 {
         step,
         captureContext,
         block: currentBlock,
+        screenshotAttached,
       });
 
       const raw = await this.callOllama({
@@ -521,6 +592,7 @@ public static class Win32 {
         model: config.ollama.model,
         prompt,
         systemPrompt,
+        images: screenshotAttached ? [screenshotBase64] : [],
       });
       const patch = normalizeAiPatch(raw);
       const updated = applyAiPatchToStep(step, patch, { target, blockId });

core/text-intel.js

@@ -673,6 +673,7 @@ function buildAiPrompt({
   step = null,
   captureContext = null,
   block = null,
+  screenshotAttached = false,
 } = {}) {
   const hasDraftTitle = step && !isPlaceholderTitle(step.title);
   const hasDraftDesc = step && Boolean(htmlToText(step.descriptionHtml || ''));
@@ -717,6 +718,7 @@ function buildAiPrompt({
       (!hasDraftTitle || target === 'description') && captureContext.titleCandidate
         ? `Suggested title: ${captureContext.titleCandidate}` : null,
     ] : []),
+    screenshotAttached ? 'Screenshot: attached to this request.' : null,
     draftTitleLine,
     draftDescLine,
   ].filter(Boolean);
@@ -770,6 +772,9 @@ function buildAiPrompt({
     richContext
       ? '- Use the OCR text, window title, app name, and element info to make the documentation specific.'
       : '- Context is limited. Use the app name or window title if available; generate a reasonable action title.',
+    screenshotAttached
+      ? '- A screenshot is attached. Use it together with the OCR and metadata to resolve visual details, but do not mention the screenshot in the output.'
+      : '- No screenshot is attached. Rely on OCR, the window title, app name, and element info.',
     '- Do NOT generate blocks that describe the technical capture process or mention OCR.',
     '- Do NOT invent details not supported by the capture context.',
     '- If the target is one block, only rewrite that block.',

docs/getting_started_with_ai.md

@@ -22,6 +22,14 @@ ollama pull llama3.2:1b
 
 That model is small enough to feel responsive on modest hardware, but still good enough for human-sounding titles and short text blocks.
 
+If you want StepForge to send the screenshot itself to the model, pull a vision-capable model instead:
+
+```bash
+ollama pull gemma3
+```
+
+That model can inspect pictures as well as text, so it is better when you want the AI to read the UI directly from the screenshot.
+
 If you need something even smaller, try:
 
 ```bash
@@ -44,7 +52,7 @@ Set:
 
 * `Enable AI text filling` to on
 * `Ollama host` to your local Ollama server
-* `Ollama model` to `llama3.2:1b` or the smaller model you pulled
+* `Ollama model` to `llama3.2:1b` for text-only mode, or `llama3.2-vision` if you want screenshot-aware AI
 
 The default host is:
 
@@ -73,3 +81,4 @@ You can also use `More -> Generate all text fields with AI` to fill the whole st
 * Capture titles are still generated automatically without AI.
 * AI generation only works when `Enable AI text filling` is turned on.
 * The app always uses local OCR around the click area first, then local AI only when you ask for it.
+* When the selected Ollama model supports vision, StepForge also sends the screenshot to the model so it can cross-check OCR and visual context.

tests/unit/placeholders.test.js

@@ -56,12 +56,14 @@ test('settings persist, deep-merge with defaults, and store global placeholders'
   assert.equal(s1.get('appearance'), DEFAULT_SETTINGS.appearance);
   s1.set('appearance', 'dark');
   s1.set('capture.delayMs', 1500);
+  s1.set('ai.ollama.model', 'qwen3:0.6b');
   s1.setGlobalPlaceholders({ Company: 'Acme', Author: 'Tyler' });
 
   // A fresh instance reads back the changed values merged over defaults.
   const s2 = new Settings(dir);
   assert.equal(s2.get('appearance'), 'dark');
   assert.equal(s2.get('capture.delayMs'), 1500);
+  assert.equal(s2.get('ai.ollama.model'), 'qwen3:0.6b');
   assert.equal(s2.get('capture.clickMarker'), DEFAULT_SETTINGS.capture.clickMarker);
   assert.deepEqual(s2.getGlobalPlaceholders(), { Company: 'Acme', Author: 'Tyler' });
 });

tests/unit/text-intel.test.js

@@ -1,5 +1,7 @@
 'use strict';
 
+const fs = require('node:fs');
+const path = require('node:path');
 const test = require('node:test');
 const assert = require('node:assert/strict');
 
@@ -309,7 +311,10 @@ test('ollama connection test reports installed models', async (t) => {
     settings: makeSettings(),
     getWindow: () => null,
     dataDir: root,
-    fetchImpl: async () => ({
+    fetchImpl: async (url) => {
+      const pathname = new URL(url).pathname;
+      if (pathname === '/api/tags') {
+        return {
           ok: true,
           json: async () => ({
             models: [
@@ -317,13 +322,101 @@ test('ollama connection test reports installed models', async (t) => {
               { name: 'qwen3:0.6b' },
             ],
           }),
+        };
+      }
+      if (pathname === '/api/show') {
+        return {
+          ok: true,
+          json: async () => ({
+            capabilities: ['completion', 'vision'],
           }),
+        };
+      }
+      throw new Error(`unexpected fetch: ${pathname}`);
+    },
   });
 
   const result = await service.testAiConnection();
   assert.equal(result.ok, true);
   assert.equal(result.installed, true);
   assert.equal(result.model, 'llama3.2:1b');
+  assert.equal(result.vision, true);
+});
+
+test('vision-capable models receive the screenshot in the chat request', async (t) => {
+  const root = makeTmpDir('text-intel-ai-vision');
+  t.after(() => rmrf(root));
+  const imagePath = path.join(root, 'step.png');
+  fs.writeFileSync(imagePath, Buffer.from('fake screenshot bytes'));
+
+  const step = createStep({
+    title: 'Old title',
+    descriptionHtml: '<p>Old text</p>',
+    image: {
+      originalPath: 'original.png',
+      workingPath: 'working.png',
+      size: { width: 10, height: 10 },
+    },
+    captureMetadata: {
+      windowTitle: 'Settings',
+      appName: 'chrome',
+      ocrText: 'Open settings',
+      titleCandidate: 'Open settings',
+      mode: 'fullscreen',
+    },
+  });
+  const fetchCalls = [];
+  const service = new TextIntelService({
+    store: {
+      settingsDir: root,
+      getGuide: () => ({ guideId: 'g1', title: 'Guide', descriptionHtml: '', stepsOrder: ['s1'] }),
+      getStep: () => step,
+      stepImagePath: () => imagePath,
+      saveStep: (_, next) => next,
+    },
+    settings: makeSettings(),
+    getWindow: () => null,
+    dataDir: root,
+    fetchImpl: async (url, init = {}) => {
+      const pathname = new URL(url).pathname;
+      fetchCalls.push({ pathname, init });
+      if (pathname === '/api/show') {
+        return {
+          ok: true,
+          json: async () => ({
+            capabilities: ['completion', 'vision'],
+          }),
+        };
+      }
+      if (pathname === '/api/chat') {
+        return {
+          ok: true,
+          json: async () => ({
+            message: {
+              content: JSON.stringify({
+                title: 'Open settings',
+                description: 'Use the AI tab.',
+              }),
+            },
+          }),
+        };
+      }
+      throw new Error(`unexpected fetch: ${pathname}`);
+    },
+  });
+
+  const result = await service.generateStepPatch({
+    guideId: 'g1',
+    stepId: 's1',
+    target: 'all',
+  });
+
+  assert.equal(result.ok, true);
+  const chatCall = fetchCalls.find((call) => call.pathname === '/api/chat');
+  assert.ok(chatCall, 'expected an Ollama chat request');
+  const body = JSON.parse(chatCall.init.body);
+  assert.deepEqual(body.messages[1].images, [fs.readFileSync(imagePath).toString('base64')]);
+  assert.match(body.messages[1].content, /Screenshot: attached/i);
 });
 
 test('invalid ollama output fails safely without saving the step', async (t) => {